
'''simple docstring'''

import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)







def     snake_case__						(   UpperCamelCase      ,UpperCamelCase=False						)			->   Union[str, Any]:
		_UpperCamelCase							:			Dict     =   []
		for i in range(config.num_hidden_layers						):
				# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
				rename_keys.append((f'''module.blocks.{i}.norm1.weight''', f'''vit.encoder.layer.{i}.layernorm_before.weight''')						)
				rename_keys.append((f'''module.blocks.{i}.norm1.bias''', f'''vit.encoder.layer.{i}.layernorm_before.bias''')						)
				rename_keys.append(
				    (f'''module.blocks.{i}.attn.proj.weight''', f'''vit.encoder.layer.{i}.attention.output.dense.weight''')						)
				rename_keys.append((f'''module.blocks.{i}.attn.proj.bias''', f'''vit.encoder.layer.{i}.attention.output.dense.bias''')						)
				rename_keys.append((f'''module.blocks.{i}.norm2.weight''', f'''vit.encoder.layer.{i}.layernorm_after.weight''')						)
				rename_keys.append((f'''module.blocks.{i}.norm2.bias''', f'''vit.encoder.layer.{i}.layernorm_after.bias''')						)
				rename_keys.append((f'''module.blocks.{i}.mlp.fc1.weight''', f'''vit.encoder.layer.{i}.intermediate.dense.weight''')						)
				rename_keys.append((f'''module.blocks.{i}.mlp.fc1.bias''', f'''vit.encoder.layer.{i}.intermediate.dense.bias''')						)
				rename_keys.append((f'''module.blocks.{i}.mlp.fc2.weight''', f'''vit.encoder.layer.{i}.output.dense.weight''')						)
				rename_keys.append((f'''module.blocks.{i}.mlp.fc2.bias''', f'''vit.encoder.layer.{i}.output.dense.bias''')						)

		# projection layer + position embeddings
		rename_keys.extend(
		    [
		        ('''module.cls_token''', '''vit.embeddings.cls_token'''),
		        ('''module.patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
		        ('''module.patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
		        ('''module.pos_embed''', '''vit.embeddings.position_embeddings'''),
		    ]						)

		if base_model:
				# layernorm + pooler
				rename_keys.extend(
				    [
				        ('''module.norm.weight''', '''layernorm.weight'''),
				        ('''module.norm.bias''', '''layernorm.bias'''),
				    ]						)

				# if just the base model, we should remove "vit" from all keys that start with "vit"
				_UpperCamelCase							:			List[str]     =   [(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''						) else pair for pair in rename_keys]
		else:
				# layernorm + classification head
				rename_keys.extend(
				    [
				        ('''norm.weight''', '''vit.layernorm.weight'''),
				        ('''norm.bias''', '''vit.layernorm.bias'''),
				        ('''head.weight''', '''classifier.weight'''),
				        ('''head.bias''', '''classifier.bias'''),
				    ]						)

		return rename_keys







def     snake_case__						(   UpperCamelCase      ,UpperCamelCase      ,UpperCamelCase=False						)			->   Optional[int]:
		for i in range(config.num_hidden_layers						):
				if base_model:
						_UpperCamelCase							:			Union[str, Any]     =   ''''''
				else:
						_UpperCamelCase							:			List[str]     =   '''vit.'''
				# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
				_UpperCamelCase							:			Optional[Any]     =   state_dict.pop(f'''module.blocks.{i}.attn.qkv.weight'''						)
				_UpperCamelCase							:			List[Any]     =   state_dict.pop(f'''module.blocks.{i}.attn.qkv.bias'''						)
				# next, add query, keys and values (in that order) to the state dict
				_UpperCamelCase							:			int     =   in_proj_weight[
				    : config.hidden_size, :
				]
				_UpperCamelCase							:			str     =   in_proj_bias[: config.hidden_size]
				_UpperCamelCase							:			List[str]     =   in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				_UpperCamelCase							:			Dict     =   in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				_UpperCamelCase							:			List[Any]     =   in_proj_weight[
				    -config.hidden_size :, :
				]
				_UpperCamelCase							:			Union[str, Any]     =   in_proj_bias[-config.hidden_size :]







def     snake_case__						(   UpperCamelCase						)			->   Optional[int]:
		_UpperCamelCase							:			Optional[Any]     =   ['''head.weight''', '''head.bias''']
		for k in ignore_keys:
				state_dict.pop(UpperCamelCase_      ,UpperCamelCase_						)







def     snake_case__						(   UpperCamelCase						)			->   List[str]:
		_UpperCamelCase							:			Dict     =   [
		    '''module.fc.fc1.weight''',
		    '''module.fc.fc1.bias''',
		    '''module.fc.bn1.weight''',
		    '''module.fc.bn1.bias''',
		    '''module.fc.bn1.running_mean''',
		    '''module.fc.bn1.running_var''',
		    '''module.fc.bn1.num_batches_tracked''',
		    '''module.fc.fc2.weight''',
		    '''module.fc.fc2.bias''',
		    '''module.fc.bn2.weight''',
		    '''module.fc.bn2.bias''',
		    '''module.fc.bn2.running_mean''',
		    '''module.fc.bn2.running_var''',
		    '''module.fc.bn2.num_batches_tracked''',
		    '''module.fc.fc3.weight''',
		    '''module.fc.fc3.bias''',
		]
		for k in ignore_keys:
				state_dict.pop(UpperCamelCase_      ,UpperCamelCase_						)







def     snake_case__						(   UpperCamelCase      ,UpperCamelCase      ,UpperCamelCase						)			->   Optional[int]:
		_UpperCamelCase							:			List[Any]     =   dct.pop(UpperCamelCase_						)
		_UpperCamelCase							:			int     =   val







def     snake_case__						(   UpperCamelCase      ,UpperCamelCase						)			->   Dict:
		_UpperCamelCase							:			List[Any]     =   ViTMSNConfig()
		_UpperCamelCase							:			Tuple     =   10_00

		_UpperCamelCase							:			int     =   '''datasets/huggingface/label-files'''
		_UpperCamelCase							:			Union[str, Any]     =   '''imagenet-1k-id2label.json'''
		_UpperCamelCase							:			List[Any]     =   json.load(open(hf_hub_download(UpperCamelCase_      ,UpperCamelCase_						)      ,'''r'''						)						)
		_UpperCamelCase							:			Union[str, Any]     =   {int(UpperCamelCase_						): v for k, v in idalabel.items()}
		_UpperCamelCase							:			Any     =   idalabel
		_UpperCamelCase							:			Dict     =   {v: k for k, v in idalabel.items()}

		if "s16" in checkpoint_url:
				_UpperCamelCase							:			Optional[Any]     =   3_84
				_UpperCamelCase							:			Union[str, Any]     =   15_36
				_UpperCamelCase							:			Any     =   6
		elif "l16" in checkpoint_url:
				_UpperCamelCase							:			List[str]     =   10_24
				_UpperCamelCase							:			Dict     =   40_96
				_UpperCamelCase							:			Tuple     =   24
				_UpperCamelCase							:			Any     =   16
				_UpperCamelCase							:			Union[str, Any]     =   0.1
		elif "b4" in checkpoint_url:
				_UpperCamelCase							:			List[Any]     =   4
		elif "l7" in checkpoint_url:
				_UpperCamelCase							:			Dict     =   7
				_UpperCamelCase							:			str     =   10_24
				_UpperCamelCase							:			Dict     =   40_96
				_UpperCamelCase							:			List[Any]     =   24
				_UpperCamelCase							:			Tuple     =   16
				_UpperCamelCase							:			int     =   0.1

		_UpperCamelCase							:			Tuple     =   ViTMSNModel(UpperCamelCase_						)

		_UpperCamelCase							:			Tuple     =   torch.hub.load_state_dict_from_url(UpperCamelCase_      ,map_location='''cpu'''						)['''target_encoder''']

		_UpperCamelCase							:			Dict     =   ViTImageProcessor(size=config.image_size						)

		remove_projection_head(UpperCamelCase_						)
		_UpperCamelCase							:			Union[str, Any]     =   create_rename_keys(UpperCamelCase_      ,base_model=UpperCamelCase_						)

		for src, dest in rename_keys:
				rename_key(UpperCamelCase_      ,UpperCamelCase_      ,UpperCamelCase_						)
		read_in_q_k_v(UpperCamelCase_      ,UpperCamelCase_      ,base_model=UpperCamelCase_						)

		model.load_state_dict(UpperCamelCase_						)
		model.eval()

		_UpperCamelCase							:			int     =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''

		_UpperCamelCase							:			int     =   Image.open(requests.get(UpperCamelCase_      ,stream=UpperCamelCase_						).raw						)
		_UpperCamelCase							:			Union[str, Any]     =   ViTImageProcessor(
		    size=config.image_size      ,image_mean=UpperCamelCase_      ,image_std=UpperCamelCase_						)
		_UpperCamelCase							:			List[Any]     =   image_processor(images=UpperCamelCase_      ,return_tensors='''pt'''						)

		# forward pass
		torch.manual_seed(2						)
		_UpperCamelCase							:			str     =   model(**UpperCamelCase_						)
		_UpperCamelCase							:			Optional[Any]     =   outputs.last_hidden_state

		# The following Colab Notebook was used to generate these outputs:
		# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
		if "s16" in checkpoint_url:
				_UpperCamelCase							:			int     =   torch.tensor([[-1.0915, -1.4876, -1.1809]]						)
		elif "b16" in checkpoint_url:
				_UpperCamelCase							:			List[Any]     =   torch.tensor([[14.28_89, -18.90_45, 11.72_81]]						)
		elif "l16" in checkpoint_url:
				_UpperCamelCase							:			Optional[Any]     =   torch.tensor([[41.50_28, -22.86_81, 45.64_75]]						)
		elif "b4" in checkpoint_url:
				_UpperCamelCase							:			Optional[Any]     =   torch.tensor([[-4.3868, 5.2932, -0.4137]]						)
		else:
				_UpperCamelCase							:			List[Any]     =   torch.tensor([[-0.1792, -0.6465, 2.4263]]						)

		# verify logits
		assert torch.allclose(last_hidden_state[:, 0, :3]      ,UpperCamelCase_      ,atol=1e-4						)

		print(f'''Saving model to {pytorch_dump_folder_path}'''						)
		model.save_pretrained(UpperCamelCase_						)

		print(f'''Saving image processor to {pytorch_dump_folder_path}'''						)
		image_processor.save_pretrained(UpperCamelCase_						)


if __name__ == "__main__":
	_UpperCAmelCase :   int			    =	argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--checkpoint_url""",
	    default="""https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar""",
	    type=str,
	    help="""URL of the checkpoint you'd like to convert.""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
	)

	_UpperCAmelCase :   Tuple			    =	parser.parse_args()
	convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	list:







 '''simple docstring'''
 UpperCamelCase			  =      len(UpperCamelCase_       )
 UpperCamelCase			  =      [[0] * n for i in range(UpperCamelCase_       )]
 for i in range(UpperCamelCase_       ):
  UpperCamelCase			  =      y_points[i]

 for i in range(2							,					UpperCamelCase_       ):
  for j in range(UpperCamelCase_							,					UpperCamelCase_       ):
   UpperCamelCase			  =      (
       (xa - x_points[j - i + 1]) * q[j][i - 1]
       - (xa - x_points[j]) * q[j - 1][i - 1]
   ) / (x_points[j] - x_points[j - i + 1])

 return [q[n - 1][n - 1], q]


if __name__ == "__main__":
     import doctest

     doctest.testmod()
